A089-0008
Development and Applications of a New Coastal Cloud and Fog Satellite-derived Albedo Record for San Clemente Island
Development and Applications of a New Coastal Cloud and Fog Satellite-derived Albedo Record for San Clemente Island
Thursday, 10 December 2020
Poster
Abstract:
San Clemente Island (SCI), located in the Southern California Bight, is owned and operated by the U.S. Navy and is home to many endemic species, as well as terrestrial plant and avian species federally listed as threatened or endangered. The SCI ecosystem is influenced by the presence of warm season low-level clouds that shade, cool and, especially when in the form of fog, moisten the environment. We created a new cloud and fog satellite-derived albedo product for SCI at a higher resolution than previous data-sets. The record spans 23 summers (1996 – 2018, May - Sep). The spatial resolution is ~1 km and the temporal resolution is half hourly (nominally 0600 PST to 1800 PST). Using GOES-WEST visible measurements, it was discovered that small (typically on the order of less than 5 km) geographical misalignment of the satellite images were common. Although not an issue in most applications in which GOES satellite visible measurements have been used, the biological ramifications of such a shift could be significant. Thus, to provide a quality 1 km product for a narrow island such as SCI it was necessary to correct any misalignment. Geographical misalignment of albedo is easily apparent in clear sky images at the interface of land and water. This concept is applied to a clear sky coastline domain and used in the automated alignment correction. The new cloud albedo record provides a wealth of new knowledge about the weather and climate of SCI during the warm season. The northwest coast of SCI is the cloudiest/foggiest area. June, on average, is the cloudiest month on SCI. The intra-day variability reaches ~ 20% cloud albedo while interannual and monthly variability are ~ 10%. Monthly mean albedo fields were found to significantly contribute to a vegetation habitat suitability model. These results serve as an initial example of the utility of the new cloud and fog record in understanding ecological phenomena and patterns.